It is becoming more common for physicians to require diagnostic quality images to be transmitted, via computer communication methods, from an image server to clients stations, at which there are physicians, over large distances. However, due to the large size of the images, the high level of detail required for diagnosis and the limited bandwidth of computer communication lines, it is difficult to transmit such diagnostic quality images. One physical difference between diagnostic quality images and other images is that diagnostic quality images usually include 9-16 bits per pixels, while regular images are usually 8 bits per pixel.
One solution, described in a PCT application “Data Distribution System”, PCT/IL97/00349, filed Oct. 29, 1997 in the Israel Receiving office, by applicant Algotec Systems Ltd., the disclosure of which is incorporated herein by reference, interacts with a physician so that only those images, regions of interest and/or detail levels which he requires are transmitted.
Another solution is to compress the images, using a lossy compression scheme. However, lossy compression may reduce or even negate the diagnostic quality of the images. The most common types of lossy image compression methods involve transforming the data using a transform into a plurality of coefficients. Typically, the transform is chosen so that most of the energy of the image is concentrated in a small number of the coefficients. The low energy coefficients are either dropped or encoded using a low resolution encoding method, thus achieving the desired compression. Typically, all the coefficients are quantized.
Wavelet compression is a relatively new type of lossy image compression, utilizing the wavelet transform. Many publications, including “An Introduction to Wavelets”, by Amara Graps, in IEEE Computational Science and Engineering Summer 1995, Vol. 2 No. 2, published by the IEEE Computer Society, Los Alamitos, California, USA, “Compressing Still and Moving Images with Wavelets”, by M. L. Hilton, B. D. Jawerth and A. Sengupta, in Multimedia Systems, Vol. 2, No. 3, “High-Resolution Still Picture Compression”, by M. V. Wickerhauser, in Digital Image Processing and “Space-Frequency Quantization for Wavelet Image Coding”, by Zixiang Xiong, Kannan Ramchandran and Michael T. Orchard, in IEEE Transactions on Image Processing, Vol. 6, No. 5, May 1997, the disclosures of which are incorporated herein by reference, describe the application of wavelet compression for lossy image compression. In addition, some of these references describe changing various aspects of the compression methods and quantization portions of the method responsive to human perceptual abilities. In addition, the use of RMSE (root mean square error) or human-weighted-RMSE for image quality evaluation is also described therein.
“Visual Thresholds for Wavelet Quantization Error”, by A. B. Watson, G. Y. Yang, J. A. Solomon and J. Villasenor, in SPIE Proceeding Vol. 2657, paper #44, Human Vision and Electronic Imaging, B. Rogowitz and J. Allebach, Ed., The Society for Imaging Science and Technology, (1996), the disclosure of which is incorporated herein by reference, describes setting wavelet quantization responsive to human visual abilities and in particular to an expected level of zooming and expected display resolution.